da Silva Rocha, É.; Endo, P.T. A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph. Appl. Sci.2022, 12, 3103.
da Silva Rocha, É.; Endo, P.T. A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph. Appl. Sci. 2022, 12, 3103.
da Silva Rocha, É.; Endo, P.T. A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph. Appl. Sci.2022, 12, 3103.
da Silva Rocha, É.; Endo, P.T. A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph. Appl. Sci. 2022, 12, 3103.
Abstract
Introduction: Dental segmentation in panoramic radiograph has become very relevant in dentistry, since it allows health professionals to carry out their assessments more clearly and helps them to define the best possible treatment plan for their patients. Objectives: In this work, a comparative study is carried out with four segmentation algorithms (U-Net, DCU-Net, DoubleU-Net and Nano-Net) that are prominent in the medical literature on segmentation and we evaluate their results with the current state of the art of dental segmentation in panoramic radiograph. Methods: These algorithms were tested with a dataset consisting of 1,500 images, considering experiment scenarios with and without augmentation data. Results: DoubleU-Net was the model that presented the best results among the analyzed models, reaching 96.591% accuracy and 92.886% Dice using the dataset with data augmentation. Another model that stood out was Nano-Net using the dataset without data augmentation; this model achieved results close to that of the literature with only 235 thousand trainable parameters, while the literature model (TSASNet) contains 78 million. Conclusions: The results obtained in this work are satisfactory and present paths for a better and more effective dental segmentation process.
Keywords
Dental segmentation; deep learning; panoramic radiograph
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
